An enhanced segmentation of blood vessels in retinal images using contourlet
Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segment...
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Published in | 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2008; pp. 3530 - 3533 |
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Main Authors | , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
United States
IEEE
01.01.2008
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Subjects | |
Online Access | Get full text |
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Summary: | Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms. |
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ISBN: | 9781424418145 1424418143 |
ISSN: | 1094-687X 1557-170X 1558-4615 |
DOI: | 10.1109/IEMBS.2008.4649967 |